Technical Papers
Jan 6, 2021

Comparison of sUAS Photogrammetry and TLS for Detecting Changes in Soil Surface Elevations Following Deep Tillage

Publication: Journal of Surveying Engineering
Volume 147, Issue 2

Abstract

Agricultural and forestry operations significantly affect land morphology. Monitoring topographical and morphological changes is important in agriculture, forestry geomorphology, and soil sciences for improving land management. Tillage is one of the most common land management methods, which aims at loosening the soil and optimizing edaphological conditions. This paper used point cloud data from a mine reclamation project in Brisbin, Pennsylvania that employed deep tillage methods to improve soil structure. In recent years, point cloud technologies such as small unmanned aerial system (sUAS) photogrammetry often has been used to support agricultural and forestry operations, and to provide detail estimation of land morphological changes. This paper assessed the effectiveness of sUAS and terrestrial laser scanning (TLS) to estimate changes in soil surface elevations after deep tillage treatment. A traditional survey with total stations was used as a reference to assess the performance of each method and gain insights. Several parameters that affect the accuracy of such multiepoch surveys were examined, including georeferencing, sUAS camera self-calibration, sUAS software, ground classification methods, the effect of vegetation on point cloud accuracy, the effect of distance from scanner on TLS point cloud accuracy, and merging scenarios to utilize the advantages of both sensors. Results indicated that in areas with low vegetation, both methods can provide reliable land surface estimation; however, in areas with dense and high vegetation, the two methods had considerable vegetation penetration issues. In TLS surveys, the error increased with increasing distance from a scanner setup. TLS achieved higher accuracy than sUAS surveys up to 5 m from a scanner setup, whereas at distances between 5 and 10 m from a scanner setup, accuracy was comparable for the two methods, and beyond 10 m from a scanner setup sUAS achieved better accuracy than TLS. Furthermore, this paper investigated the benefit of using a prior camera self-calibration, estimated on the same site from the first sUAS data set before tillage, versus estimating a new self-calibration for the second sUAS data sets after tillage. Results showed that a prior self-calibration is essential when the number of ground control points (GCPs) is low, i.e., fewer than four GCPs when Global Navigation Satellite System real-time kinematic (GNSS-RTK) is available, and fewer than eight GCPs when GNSS-RTK is not available. Insights gained from this study can assist in improving surveying planning, and they are important to surveyors and practitioners who are employed in agricultural and forestry applications.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The following surveying engineering students are acknowledged for their contribution in the data collection: Vincent Pavill, John Chapman, Brian Springer, Zachary Kenzakoski, and David Neilson. We thank Paul Kitko for giving us access to the site and Jacob Johnson for assisting with placing the rebars.

References

Agisoft. 2019. “Agisoft Metashape user manual, professional edition, version 1.5.” Accessed July 23, 2020. https://www.agisoft.com/pdf/metashape-pro_1_5_en.pdf.
Alba, M., A. Giussani, F. Roncoroni, and M. Scaioni. 2007. “Review and comparison of techniques for terrestrial 3D-view georeferencing.” In Proc., 5th Int. Symp. on Mobile Mapping Technology. Padua, Italy: International Society for Photogrammetry and Remote Sensing.
Alidoost, F., and H. Arefi. 2017. “Comparison of UAS-based photogrammetry software for 3D point cloud generation: A survey over a historical site.” In Proc., ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4. Göttingen, Germany: Copernicus GmbH.
Anders, N., J. Valente, R. Masselink, and S. Keesstra. 2019. “Comparing filtering techniques for removing vegetation from UAV-based photogrammetric point clouds.” Drones 3 (3): 61. https://doi.org/10.3390/drones3030061.
Ashby, W. C. 1997. “Soil ripping and herbicides enhance tree and shrub restoration on stripmines.” Restor. Ecol. 5 (2): 169–177. https://doi.org/10.1046/j.1526-100X.1997.09720.x.
Ashcroft, M. B., J. R. Gollan, and D. Ramp. 2014. “Creating vegetation density profiles for a diverse range of ecological habitats using terrestrial laser scanning.” Methods Ecol. Evol. 5 (3): 263–272. https://doi.org/10.1111/2041-210X.12157.
ASPRS (American Society for Photogrammetry and Remote Sensing). 2015. “ASPRS positional accuracy standards for digital geospatial data.” Photogramm. Eng. Remote Sens. 81 (3): 1–26. https://doi.org/10.14358/PERS.81.3.A1-A26.
Barnhart, T. B., and B. T. Crosby. 2013. “Comparing two methods of surface change detection on an evolving thermokarst using high-temporal-frequency terrestrial laser scanning, Selawik River, Alaska.” Remote Sens. 5 (6): 2813–2837. https://doi.org/10.3390/rs5062813.
Bolkas, D. 2019. “Assessment of GCP number and separation distance for small UAS surveys with and without GNSS-PPK positioning.” J. Surv. Eng. 145 (3): 04019007. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000283.
Bolkas, D., G. Fotopoulos, A. Braun, and I. N. Tziavos. 2016. “Assessing digital elevation model uncertainty using GPS survey data.” J. Surv. Eng. 142 (3): 04016001. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000169.
Bolkas, D., and A. Martinez. 2018. “Effect of target color and scanning geometry on terrestrial LiDAR point-cloud noise and plane fitting.” J. Appl. Geod. 12 (1): 109–127. https://doi.org/10.1515/jag-2017-0034.
Bolkas, D., T. J. Sichler, and W. McMarlin. 2019. “A case study on the accuracy assessment of a small UAS photogrammetric survey using terrestrial laser scanning.” Surv. Land Inf. Sci. 78 (1): 31–44.
Brodu, N., and D. Lague. 2012. “3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology.” ISPRS J. Photogramm. Remote Sens. 68 (Mar): 121–134. https://doi.org/10.1016/j.isprsjprs.2012.01.006.
Carlson, C. A., T. R. Fox, S. R. Colbert, D. L. Kelting, H. L. Allen, and T. J. Albaugh. 2006. “Growth and survival of Pinus taeda in response to surface and subsurface tillage in the southeastern United States.” For. Ecol. Manage. 234 (1–3): 209–217. https://doi.org/10.1016/j.foreco.2006.07.002.
Casella, V., F. Chiabrando, M. Franzini, and A. M. Manzino. 2020. “Accuracy assessment of a UAV block by different software packages, processing schemes and validation strategies.” ISPRS Int. J. Geo-Inf. 9 (3): 164. https://doi.org/10.3390/ijgi9030164.
Che, E., and M. J. Olsen. 2017. “Fast ground filtering for TLS data via scanline density analysis.” ISPRS J. Photogramm. Remote Sens. 129 (Jul): 226–240. https://doi.org/10.1016/j.isprsjprs.2017.05.006.
CloudCompare. 2015. “CloudCompare version 2.6.1 user manual.” Accessed October 20, 2020. http://www.cloudcompare.org/doc/qCC/CloudCompare%20v2.6.1%20-%20User%20manual.pdf.
Cramer, M., H.-J. Przybilla, and A. Zurhorst. 2017. “UAV cameras: Overview and geometric calibration benchmark.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42 (2): 85. https://doi.org/10.5194/isprs-archives-XLII-2-W6-85-2017.
Du, Z., T. Ren, C. Hu, and Q. Zhang. 2015. “Transition from intensive tillage to no-till enhances carbon sequestration in microaggregates of surface soil in the North China Plain.” Soil Tillage Res. 146 (Part A): 26–31. https://doi.org/10.1016/j.still.2014.08.012.
Eltner, A., A. Kaiser, C. Castillo, G. Rock, F. Neugirg, and A. Abellán. 2016. “Image-based surface reconstruction in geomorphometry–merits, limits and developments.” Earth Surf. Dyn. 4 (2): 359–389. https://doi.org/10.5194/esurf-4-359-2016.
Fan, L., J. A. Smethurst, P. M. Atkinson, and W. Powrie. 2015. “Error in target-based georeferencing and registration in terrestrial laser scanning.” Comput. Geosci. 83 (Oct): 54–64. https://doi.org/10.1016/j.cageo.2015.06.021.
Fanigliulo, R., F. Antonucci, S. Figorilli, D. Pochi, F. Pallottino, L. Fornaciari, R. Grilli, and C. Costa. 2020. “Light drone-based application to assess soil tillage quality parameters.” Sensors 20 (3): 728. https://doi.org/10.3390/s20030728.
Fiener, P., et al. 2018. “Uncertainties in assessing tillage erosion–How appropriate are our measuring techniques?” Geomorphology 304 (Mar): 214–225. https://doi.org/10.1016/j.geomorph.2017.12.031.
Forlani, G., E. Dall’Asta, F. Diotri, U. Morra di Cella, R. Roncella, and M. Santise. 2018. “Quality assessment of DSMs produced from UAV flights georeferenced with on-board RTK positioning.” Remote Sens. 10 (2): 311. https://doi.org/10.3390/rs10020311.
Gabrlik, P., A. la Cour-Harbo, P. Kalvodova, L. Zalud, and P. Janata. 2018. “Calibration and accuracy assessment in a direct georeferencing system for UAS photogrammetry.” Int. J. Remote Sens. 39 (15–16): 4931–4959. https://doi.org/10.1080/01431161.2018.1434331.
Gneeniss, A. S., J. P. Mills, and P. E. Miller. 2015. “In-flight photogrammetric camera calibration and validation via complementary lidar.” ISPRS J. Photogramm. Remote Sens. 100 (Feb): 3–13. https://doi.org/10.1016/j.isprsjprs.2014.04.019.
Gruszczyński, W., W. Matwij, and P. Ćwiąkacła. 2017. “Comparison of low-altitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation.” ISPRS J. Photogramm. Remote Sens. 126 (Apr): 168–179. https://doi.org/10.1016/j.isprsjprs.2017.02.015.
Hugenholtz, C. H., K. Whitehead, O. W. Brown, T. E. Barchyn, B. J. Moorman, A. LeClair, K. Riddell, and T. Hamilton. 2013. “Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model.” Geomorphology 194 (Jul): 16–24. https://doi.org/10.1016/j.geomorph.2013.03.023.
Javadnejad, F., R. K. Slocum, D. T. Gillins, M. J. Olsen, and C. E. Parrish. 2021. “Dense point cloud quality factor as proxy for accuracy assessment of image-based 3D reconstruction.” J. Surv. Eng. 147 (1): 04020021. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000333.
Jensen, J. L. R., and A. J. Mathews. 2016. “Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem.” Remote Sens. 8 (1): 50. https://doi.org/10.3390/rs8010050.
Jeon, E.-I., S.-J. Yu, H.-W. Seok, S.-J. Kang, K.-Y. Lee, and O.-S. Kwon. 2017. “Comparative evaluation of commercial softwares in UAV imagery for cultural heritage recording: Case study for traditional building in South Korea.” Spatial Inf. Res. 25 (5): 701–712. https://doi.org/10.1007/s41324-017-0137-z.
Lague, D., N. Brodu, and J. Leroux. 2013. “Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z).” ISPRS J. Photogramm. Remote Sens. 82 (Aug): 10–26. https://doi.org/10.1016/j.isprsjprs.2013.04.009.
Lau, C. L., S. Halim, M. Zulkepli, A. M. Azwan, W. L. Tang, and A. K. Chong. 2015. “Terrain extraction by integrating terrestrial laser scanner data and spectral information.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 40 (1): 45. https://doi.org/10.5194/isprsarchives-XL-2-W4-45-2015.
Lindstrom, M. J. 2002. “Tillage erosion: Description and process.” In Encyclopedia of soil science, 1324–1326. New York: Marcel Dekker.
Löf, M., D. C. Dey, R. M. Navarro, and D. F. Jacobs. 2012. “Mechanical site preparation for forest restoration.” New For. 43 (5–6): 825–848. https://doi.org/10.1007/s11056-012-9332-x.
Mårtensson, S.-G., and Y. Reshetyuk. 2017. “Height uncertainty in digital terrain modelling with unmanned aircraft systems.” Surv. Rev. 49 (355): 312–318. https://doi.org/10.1080/00396265.2016.1180754.
Mehra, P., J. Baker, R. E. Sojka, N. Bolan, J. Desbiolles, M. B. Kirkham, C. Ross, and R. Gupta. 2018. “A review of tillage practices and their potential to impact the soil carbon dynamics.” In Vol. 150 of Advances in agronomy, edited by D. L. Sparks, 185–230. Cambridge, MA: Academic Press.
Meijer, A. D., J. L. Heitman, J. G. White, and R. E. Austin. 2013. “Measuring erosion in long-term tillage plots using ground-based lidar.” Soil Tillage Res. 126 (Jan): 1–10. https://doi.org/10.1016/j.still.2012.07.002.
Meng, X., N. Shang, X. Zhang, C. Li, K. Zhao, X. Qiu, and E. Weeks. 2017. “Photogrammetric UAV mapping of terrain under dense coastal vegetation: An object-oriented classification ensemble algorithm for classification and terrain correction.” Remote Sens. 9 (11): 1187. https://doi.org/10.3390/rs9111187.
Moudrý, V., K. Gdulová, M. Fogl, P. Klápště, R. Urban, J. Komárek, L. Moudrá, M. Štroner, V. Barták, and M. Solský. 2019. “Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success.” Appl. Geogr. 104 (Mar): 32–41. https://doi.org/10.1016/j.apgeog.2019.02.002.
Mukupa, W., G. W. Roberts, C. M. Hancock, and K. Al-Manasir. 2017. “A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures.” Surv. Rev. 49 (353): 99–116. https://doi.org/10.1080/00396265.2015.1133039.
Mulla, D. J. 2013. “Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps.” Biosyst. Eng. 114 (4): 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009.
Nunes, M. R., J. E. Denardin, E. A. Pauletto, A. Faganello, and L. F. S. Pinto. 2015. “Mitigation of clayey soil compaction managed under no-tillage.” Soil Tillage Res. 148 (May): 119–126. https://doi.org/10.1016/j.still.2014.12.007.
Ouédraogo, M. M., A. Degré, C. Debouche, and J. Lisein. 2014. “The evaluation of unmanned aerial system-based photogrammetry and terrestrial laser scanning to generate DEMs of agricultural watersheds.” Geomorphology 214 (Jun): 339–355. https://doi.org/10.1016/j.geomorph.2014.02.016.
Panholzer, H., and A. Prokop. 2013. “Wedge-filtering of geomorphologic terrestrial laser scan data.” Sensors 13 (2): 2579–2594. https://doi.org/10.3390/s130202579.
Pareja-Sánchez, E., D. Plaza-Bonilla, M. C. Ramos, J. Lampurlanés, J. Álvaro-Fuentes, and C. Cantero-Martínez. 2017. “Long-term no-till as a means to maintain soil surface structure in an agroecosystem transformed into irrigation.” Soil Tillage Res. 174 (Dec): 221–230. https://doi.org/10.1016/j.still.2017.07.012.
Pirotti, F., A. Guarnieri, and A. Vettore. 2013. “Ground filtering and vegetation mapping using multi-return terrestrial laser scanning.” ISPRS J. Photogramm. Remote Sens. 76 (Feb): 56–63. https://doi.org/10.1016/j.isprsjprs.2012.08.003.
Remondino, F., E. Nocerino, I. Toschi, and F. Menna. 2017. “A critical review of automated photogrammetric processing of large datasets.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42 (2): 591–599.
Salach, A., K. Bakuła, M. Pilarska, W. Ostrowski, K. Górski, and Z. Kurczyński. 2018. “Accuracy assessment of point clouds from LiDAR and dense image matching acquired using the UAV platform for DTM creation.” ISPRS Int. J. Geo-Inf. 7 (9): 342. https://doi.org/10.3390/ijgi7090342.
Sheehy, J., K. Regina, L. Alakukku, and J. Six. 2015. “Impact of no-till and reduced tillage on aggregation and aggregate-associated carbon in Northern European agroecosystems.” Soil Tillage Res. 150 (Jul): 107–113. https://doi.org/10.1016/j.still.2015.01.015.
Stathopoulou, E.-K., M. Welponer, and F. Remondino. 2019. “Open-source image-based 3D reconstruction pipelines: Review, comparison and evaluation.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42 (2): 331–338. https://doi.org/10.5194/isprs-archives-XLII-2-W17-331-2019.
Suzuki, L. E. A. S., J. M. Reichert, and D. J. Reinert. 2013. “Degree of compactness, soil physical properties and yield of soybean in six soils under no-tillage.” Soil Res. 51 (4): 311–321. https://doi.org/10.1071/SR12306.
Tarolli, P. 2014. “High-resolution topography for understanding earth surface processes: Opportunities and challenges.” Geomorphology 216 (Jul): 295–312. https://doi.org/10.1016/j.geomorph.2014.03.008.
Tarolli, P., M. Cavalli, and R. Masin. 2019. “High-resolution morphologic characterization of conservation agriculture.” Catena 172 (Jan): 846–856. https://doi.org/10.1016/j.catena.2018.08.026.
Teza, G., A. Galgaro, N. Zaltron, and R. Genevois. 2007. “Terrestrial laser scanner to detect landslide displacement fields: a new approach.” Int. J. Remote Sens. 28 (16): 3425–3446. https://doi.org/10.1080/01431160601024234.
Triplett, G. B., Jr., and W. A. Dick. 2008. “No-tillage crop production: A revolution in agriculture!” Agron. J. 100: 153. https://doi.org/10.2134/agronj2007.0005c.
Tsouros, D. C., S. Bibi, and P. G. Sarigiannidis. 2019. “A review on UAV-based applications for precision agriculture.” Information 10 (11): 349. https://doi.org/10.3390/info10110349.
Turner, D., A. Lucieer, and S. M. De Jong. 2015. “Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV).” Remote Sens. 7 (2): 1736–1757. https://doi.org/10.3390/rs70201736.
Turunen, M., E. Turtola, M. T. Vaaja, J. Hyväluoma, and H. Koivusalo. 2020. “Terrestrial laser scanning data combined with 3D hydrological modeling decipher the role of tillage in field water balance and runoff generation.” Catena 187 (Apr): 104363. https://doi.org/10.1016/j.catena.2019.104363.
Wang, J. 2013. “Block-to-point fine registration in terrestrial laser scanning.” Remote Sens. 5 (12): 6921–6937. https://doi.org/10.3390/rs5126921.
Watanabe, Y., and Y. Kawahara. 2016. “UAV photogrammetry for monitoring changes in river topography and vegetation.” Procedia Eng. 154: 317–325. https://doi.org/10.1016/j.proeng.2016.07.482.
Yang, C., Z.-A. Su, J.-R. Fan, H.-D. Fang, L.-T. Shi, J.-H. Zhang, Z.-Y. He, T. Zhou, and X.-Y. Wang. 2020. “Simulation of the landform change process on a purple soil slope due to tillage erosion and water erosion using UAV technology.” J. Mountain Sci. 17 (6): 1333–1344. https://doi.org/10.1007/s11629-019-5869-x.
Yurtseven, H. 2019. “Comparison of GNSS-, TLS- and different altitude UAV-generated datasets on the basis of spatial differences.” ISPRS Int. J. Geo-Inf. 8 (4): 175. https://doi.org/10.3390/ijgi8040175.
Zhang, W., J. Qi, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan. 2016. “An easy-to-use airborne LiDAR data filtering method based on cloth simulation.” Remote Sens. 8 (6): 501. https://doi.org/10.3390/rs8060501.

Information & Authors

Information

Published In

Go to Journal of Surveying Engineering
Journal of Surveying Engineering
Volume 147Issue 2May 2021

History

Received: Aug 19, 2020
Accepted: Oct 26, 2020
Published online: Jan 6, 2021
Published in print: May 1, 2021
Discussion open until: Jun 6, 2021

Permissions

Request permissions for this article.

Authors

Affiliations

Assistant Professor, Dept. of Surveying Engineering, Pennsylvania State Univ., Wilkes-Barre Campus, 44 University Dr., Dallas, PA 18612 (corresponding author). ORCID: https://orcid.org/0000-0002-1269-8704. Email: [email protected]
Lecturer, Dept. of Civil and Environmental Engineering, Pennsylvania State Univ., University Park, 44 University Dr., State College, PA 16802. ORCID: https://orcid.org/0000-0002-9392-7344. Email: [email protected]
Professor, Dept. of Ecosystem Science and Management, Pennsylvania State Univ., University Park, 44 University Dr., State College, PA 16802. ORCID: https://orcid.org/0000-0002-0336-4534. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share